TY - GEN
T1 - Ants can learn from the opposite
AU - Rojas-Morales, Nicolás
AU - María-Cristina, Riff R.
AU - Montero, Elizabeth
N1 - Funding Information:
Supported by CONICYT-PCHA/National Doctorate/2015-21150696. Supported by FONDECYT Project No. 1151456 Partially supported by the Centro Cient?fico Tecnologico de Valpara?so (CCTVal) Project No. FB0821 Supported by FONDECYT Initiation into Research Project no. 11150787 We would like to thank Christine Solnon for providing us the code for performing our experiments.
PY - 2016/7/20
Y1 - 2016/7/20
N2 - In this work we present different learning strategies focused on detecting candidate solutions that are not interesting to be explored by a metaheuristic, in terms of evaluation function. We include a first step before the metaheuris-tic. The information obtained from this step is given to the metaheuristic, for visiting candidate solutions that are more promising in terms of their quality. The goal of using these strategies is to learn about candidate solutions that can be discarded from the search space, and thus to improve the search of the metaheuristic. We present two new strategies that differ on how the solutions can be constructed in an opposite way. Our approach is evaluated using Ant Solver, a well-known ant based algorithm for solving Constraint Satisfaction Problems. We show promising results that make our solution as good approach to apply in other metaheuristics.
AB - In this work we present different learning strategies focused on detecting candidate solutions that are not interesting to be explored by a metaheuristic, in terms of evaluation function. We include a first step before the metaheuris-tic. The information obtained from this step is given to the metaheuristic, for visiting candidate solutions that are more promising in terms of their quality. The goal of using these strategies is to learn about candidate solutions that can be discarded from the search space, and thus to improve the search of the metaheuristic. We present two new strategies that differ on how the solutions can be constructed in an opposite way. Our approach is evaluated using Ant Solver, a well-known ant based algorithm for solving Constraint Satisfaction Problems. We show promising results that make our solution as good approach to apply in other metaheuristics.
KW - Ant algorithms
KW - Antipheromone
KW - Negative pheromone
KW - Opposite learning strategies
UR - http://www.scopus.com/inward/record.url?scp=84985914541&partnerID=8YFLogxK
U2 - 10.1145/2908812.2908927
DO - 10.1145/2908812.2908927
M3 - Conference contribution
AN - SCOPUS:84985914541
T3 - GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
SP - 389
EP - 396
BT - GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
A2 - Friedrich, Tobias
PB - Association for Computing Machinery, Inc
T2 - 2016 Genetic and Evolutionary Computation Conference, GECCO 2016
Y2 - 20 July 2016 through 24 July 2016
ER -